Optimal parameters estimation of the proton exchange membrane fuel cell stacks using a combined owl search algorithm

ABSTRACT

PEMFC (the proton exchange membrane fuel cell) is an extremely effective energy converter system that generates electricity through electrochemical reactions. Optimizing the design of PEMFC stacks is important for their proper utilization and construction. In the current essay, we present a novel strategy for estimating optimal model parameters of proton exchange membrane fuel cell stacks utilizing the COSA (Combined Owl Search Algorithm), an improved metaheuristic algorithm. We compared the performance of COSA against other published methods, comprising COA, TLBO, BH, the original OSA, and BBO by employing them in three different cost functions. The results show that COSA achieved the minimum error values compared to the other algorithms for all case studies, with error values of 0.01, 1.20, and 0.93 for BCS 500W PEMFC, NedStackPS6, and SR-12 500 W PEMFC stack models, respectively. Our proposed approach could be utilized to optimize other kinds of fuel cells and integrated with other optimization strategies to improve its upcoming efficiency. Future work can focus on simplifying the algorithm structure to reduce its complexity during the optimization process. Overall, the COSA algorithm provides an effective tool for achieving more efficient and optimal fuel cell stacks.

KEYWORDS:

• Model parameters
• estimation
• proton exchange membrane fuel cell
• SR-12 500 W, BCS 500W, NedStackPS6
• combined owl search algorithm

Acknowledgements

This research was supported by the key scientific research project of Hunan Province “Research on Virtual Anchor Technology Based on Generative Adversarial Network in the era of intelligent Media”, project number: 22A0703.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Keke Yuan

Keke Yuan was born in Hunan, China, in 1982. She received her Master's Degree in Software Engineering from Central South University in Changsha, China. She is currently a Associate Professor in Hunan University of Information Technology. Her research interest is mainly in the area of Intelligent perception and control, and artificial intelligence. She has published 13 papers in scholarly journals in the above research areas. She holds four chinese patents and two software copyrights.

Yingying Ma

Yingying Ma was born in Hunan, China, in 1984. She received her Master's Degree in Software Engineering from Hunan University in Changsha, China. She is currently a Senior Experimentalist in Hunan University of Information Technology. Her research interest is mainly in the area of information collaboration and distributed computing, and computer science and application. She has published 17 papers in scholarly journals and conferrence proceedings in the above research areas. She holds three chinese patents and four software copyrights.

Hua Zhang

Hua Zhang was born in Hunan, China, in 1976. She received her Master's Degree in Software Engineering from University of Electronic Science and Technology in Chengdu, China. She is currently a Associate Professor in Hunan University of Information Technology. Her research interest is mainly in the area of image recognition and processing, and computer science and application. She has published 25 papers in scholarly journals and conferrence proceedings in the above research areas. She holds four chinese patents and three software copyrights.

Navid Razmjooy

Dr. Navid Razmjooy holds a Ph.D. in Electrical Engineering (Control and Automation) from Tafresh University, Iran (2018). B. Sc. by Ardabil branch of IAU University, Iran (2007), M.Sc. from Isfahan branch of IAU University, Iran with honor in Mechatronics Engineering (2011), and a research opportunity internship in Amirkabir University of Technology (2017-2018). He was born in 1988. He is working in the following subjects: Renewable Energies, Control, Interval analysis, Optimization, Image Processing, Machine Vision, Soft Computing, Data Mining, Evolutionary Algorithms, and System Control. He is a senior member of IEEE/USA and YRC in IAU/Iran. He has been ranked among the world's top 2% scientists in the world based on the Stanford University/Scopus database. He published more than 150 papers and 6 books in English and Farsi in peer-reviewed journals and conferences and is now a reviewer in several national and international journals and conferences which can be found in https://publons.com/researcher/1595287/navid-razmjooy/.

Noradin Ghadimi

Noradin Ghadimi received the Ph.D. degree in Electrical Engineering from Islamic Azad University, Iran in 2018. His research interests are in the application of artificial intelligence and heuristic optimization methods to power system control design, operation and planning and power system restructuring. He has authored and co-authored of 4 books in Electrical Engineering area all in Farsi, one book and 2 book chapters in international publishers and more than 160 papers in international journals and conference proceedings. Also, he collaborates with several international journals as reviewer boards and works as editorial committee of three international journals. He has served on several other committees and panels in governmental, industrial, and technical conferences. He was selected as a distinguished researcher of the Islamic Azad University several times. In 2013, 2014, 2019, and 2020 he was also elected as a distinguished researcher in the engineering field in the Ardabil province of Iran. Furthermore, he has been included in the (highly cited researchers) Thomson Reuters’ list of the top one percent of most-cited technical Engineering scientists in 2019 -2020-21, respectively.